SoroushVahidi/scidocs huggingface dataset:
The one-time pad revisited
(2013)

The one-time pad, the mother of all encryption schemes, is well known to be information-theoretically secure, in contrast to most encryption schemes used in practice, which are at most computationally secure. In this paper, we focus on another, completely different aspect in which the one-time pad is superior to normal encryption, and which surfaces only when the receiver (not only the eavesdropper) is considered potentially dishonest, as can be the case in a larger protocol context in which encryption is used as a sub-protocol. For example, such a dishonest receiver (who is, say, coerced by the eavesdropper) can in normal encryption verifiably leak the message to the eavesdropper by revealing the secret key. While this leakage feature can provably not be avoided completely, it is more limited if the one-time pad is used. We use the constructive cryptography framework to make these statements precise.
Help your mobile applications with fog computing
(2015)

Cloud computing has paved a way for resource-constrained mobile devices to speed up their computing tasks and to expand their storage capacity. However, cloud computing is not necessary a panacea for all mobile applications. The high network latency to cloud data centers may not be ideal for delay-sensitive applications while storing everything on public clouds risks users' security and privacy. In this paper, we discuss two preliminary ideas, one for mobile application offloading and the other for mobile storage expansion, by leveraging the edge intelligence offered by fog computing to help mobile applications. Preliminary experiments conducted based on implemented prototypes show that fog computing can provide an effective and sometimes better alternative to help mobile applications.
Bayesian Biosurveillance of Disease Outbreaks
(2004)

Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.
Improving Social Inclusion Using NLP : Tools and Resources
(2016)

This paper discusses the issue of accessibility to being online and using social media for people with a learning disability, and the challenges to using a co-production approach in an accessible technology project. While an increasing number of daily living tasks are now completed online, people with a learning disability frequently experience digital exclusion due to limited literacy and IT skills. The Able to Include project sought to engage people with a learning disability as active partners to test and feedback on the use and development of a pictogram app used to make social media more accessible. The challenges mainly related to the feedback needing to be sent electronically to the partners; there was only minimal contact with them and no face to face contact. The paper also outlines how other challenges were overcome to enable genuine and meaningful co-production. These included addressing online safety and ethical issues regarding anonymity.
Mode Regularized Generative Adversarial
(2016)

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.
Abnormal brain chemistry in chronic back pain: an in vivo proton magnetic resonance spectroscopy study
(2000)

The neurobiology of chronic pain, including chronic back pain, is unknown. Structural imaging studies of the spine cannot explain all cases of chronic back pain. Functional brain imaging studies indicate that the brain activation patterns are different between chronic pain patients and normal subjects, and the thalamus, and prefrontal and cingulate cortices are involved in some types of chronic pain. Animal models of chronic pain suggest abnormal spinal cord chemistry. Does chronic pain cause brain chemistry changes? We examined brain chemistry changes in patients with chronic back pain using in vivo single- voxel proton magnetic resonance spectroscopy ((1)H-MRS). In vivo (1)H-MRS was used to measure relative concentrations of N-acetyl aspartate, creatine, choline, glutamate, glutamine, gamma-aminobutyric acid, inositol, glucose and lactate in relation to the concentration of creatine. These measurements were performed in six brain regions of nine chronic low back pain patients and 11 normal volunteers. All chronic back pain subjects underwent clinical evaluation and perceptual measures of pain and anxiety. We show that chronic back pain alters the human brain chemistry. Reductions of N-acetyl aspartate and glucose were demonstrated in the dorsolateral prefrontal cortex. Cingulate, sensorimotor, and other brain regions showed no chemical concentration differences. In chronic back pain, the interrelationship between chemicals within and across brain regions was abnormal, and there was a specific relationship between regional chemicals and perceptual measures of pain and anxiety. These findings provide direct evidence of abnormal brain chemistry in chronic back pain, which may be useful in diagnosis and future development of more effective pharmacological treatments.
Overview of the TAC 2010 Knowledge Base Population Track
(2010)

In this paper we give an overview of the Knowledge Base Population (KBP) track at TAC 2010. The main goal of KBP is to promote research in discovering facts about entities and expanding a structured knowledge base with this information. A large source collection of newswire and web documents is provided for systems to discover information. Attributes (a.k.a. “slots”) derived from Wikipedia infoboxes are used to create the reference knowledge base (KB). KBP2010 includes the following four tasks: (1) Regular Entity Linking, where names must be aligned to entities in the KB; (2) Optional Entity linking, without using Wikipedia texts; (3) Regular Slot Filling, which requires a system to automatically discover the attributes of specified entities from the source document collection and use them to expand the KB; (4) Surprise Slot Filling, which requires a system to return answers regarding new slot types within a short time period. KBP2010 has attracted many participants (over 45 teams registered for KBP 2010 (not including the RTEKBP Validation Pilot task), among which 23 teams submitted results). In this paper we provide an overview of the task definition and annotation challenges associated with KBP2010. Then we summarize the evaluation results and discuss the lessons that we have learned based on detailed analysis.
Probabilistic Similarity Logic
(2010)

Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect–the need to reason about similarities–which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domainspecific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.
A short introduction to probabilistic soft logic
(2012)

Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This paper provides an overview of the PSL language and its techniques for inference and weight learning. An implementation of PSL is available at http://psl.umiacs.umd.edu/.
Second-order cone programming
(2003)

Second-order cone programming (SOCP) problems are convex optimization problems in which a linear function is minimized over the intersection of an affine linear manifold with the Cartesian product of second-order (Lorentz) cones. Linear programs, convex quadratic programs and quadratically constrained convex quadratic programs can all be formulated as SOCP problems, as can many other problems that do not fall into these three categories. These latter problems model applications from a broad range of fields from engineering, control and finance to robust optimization and combinatorial optimization. On the other hand semidefinite programming (SDP)—that is the optimization problem over the intersection of an affine set and the cone of positive semidefinite matrices—includes SOCP as a special case. Therefore, SOCP falls between linear (LP) and quadratic (QP) programming and SDP. Like LP, QP and SDP problems, SOCP problems can be solved in polynomial time by interior point methods. The computational effort per iteration required by these methods to solve SOCP problems is greater than that required to solve LP and QP problems but less than that required to solve SDP’s of similar size and structure. Because the set of feasible solutions for an SOCP problem is not polyhedral as it is for LP and QP problems, it is not readily apparent how to develop a simplex or simplex-like method for SOCP. While SOCP problems can be solved as SDP problems, doing so is not advisable both on numerical grounds and computational complexity concerns. For instance, many of the problems presented in the survey paper of Vandenberghe and Boyd [VB96] as examples of SDPs can in fact be formulated as SOCPs and should be solved as such. In §2, 3 below we give SOCP formulations for four of these examples: the convex quadratically constrained quadratic programming (QCQP) problem, problems involving fractional quadratic functions such as those that arise in structural optimization, logarithmic Tchebychev approximation and the
Whitening Black-Box Neural Networks
(2018)

Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks – we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.
Development of a superior frontal–intraparietal network for visuo-spatial working memory
(2006)

Working memory capacity increases throughout childhood and adolescence, which is important for the development of a wide range of cognitive abilities, including complex reasoning. The spatial-span task, in which subjects retain information about the order and position of a number of objects, is a sensitive task to measure development of spatial working memory. This review considers results from previous neuroimaging studies investigating the neural correlates of this development. Older children and adolescents, with higher capacity, have been found to have higher brain activity in the intraparietal cortex and in the posterior part of the superior frontal sulcus, during the performance of working memory tasks. The structural maturation of white matter has been investigated by diffusion tensor magnetic resonance imaging (DTI). This has revealed several regions in the frontal lobes in which white matter maturation is correlated with the development of working memory. Among these is a superior fronto-parietal white matter region, located close to the grey matter regions that are implicated in the development of working memory. Furthermore, the degree of white matter maturation is positively correlated with the degree of cortical activation in the frontal and parietal regions. This suggests that during childhood and adolescence, there is development of networks related to specific cognitive functions, such as visuo-spatial working memory. These networks not only consist of cortical areas but also the white matter tracts connecting them. For visuo-spatial working memory, this network could consist of the superior frontal and intraparietal cortex.
Nobel Lecture: The Economic Way of Looking at Behavior
(2007)

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
Foot Step Based Person Identification Using Histogram Similarity and Wavelet Decomposition
(2008)

Research in person identification and authentication has attracted significant attention from the researchers and scientists. This paper presents a biometric user authentication based on a person's foot step. The advantage of this recognition method over other types of biometrics is that it enables unobtrusive user authentication where other types of biometrics are not available. Firstly the ground reaction force data was extracted using force plate to gather ground reaction force for individuals. Later we utilized the discrete wavelet transform to de-noise the experimental data and in the final step, histograms were used to identify different person's foot step. The experimental results show improvements in identification accuracies compared to previously reported work.
Pharmaceutical cold chain management: Platform based on a distributed ledger
(2018)

In the process of distributing pharmaceutical products multiple stages are involved until products are delivered to patient. A reliable way for validating and verifying that products had been maintained within a licensed range is required. The paper presents a solution for pharmaceutical cold chain management using distributed ledger technologies. An application framework is proposed for shipment tracking which will deliver information to all stakeholders during the distribution phase of pharmaceutical products. The solution is based on Hyperledger Sawtooth distributed ledger framework, which has been extended with a custom transactions family and a sensors gateway for automatically collecting data from temperature tracking devices. The focus of the paper is to describe the data model and how entities of the system communicate.
Fiat-Shamir with Aborts: Applications to Lattice and Factoring-Based Signatures
(2009)

We demonstrate how the framework that is used for creating efficient number-theoretic ID and signature schemes can be transferred into the setting of lattices. This results in constructions of the most efficient to-date identification and signature schemes with security based on the worst-case hardness of problems in ideal lattices. In particular, our ID scheme has communication complexity of around 65, 000 bits and the length of the signatures produced by our signature scheme is about 50, 000 bits. All prior lattice-based identification schemes required on the order of millions of bits to be transferred, while all previous lattice-based signature schemes were either stateful, too inefficient, or produced signatures whose lengths were also on the order of millions of bits. The security of our identification scheme is based on the hardness of finding the approximate shortest vector to within a factor of Õ(n) in the standard model, while the security of the signature scheme is based on the same assumption in the random oracle model. Our protocols are very efficient, with all operations requiring Õ(n) time. We also show that the technique for constructing our lattice-based schemes can be used to improve certain number-theoretic schemes. In particular, we are able to shorten the length of the signatures that are produced by Girault’s factoring-based digital signature scheme ([10,11,31]).
Arabic Optical Character Recognition Software : A Review 1
(2017)

This paper provides a thorough evaluation of a set of six important Arabic OCR systems available in the market; namely: Abbyy FineReader, Leadtools, Readiris, Sakhr, Tesseract and NovoVerus. We test the OCR systems using a randomly selected images from the well known Arabic Printed Text Image database (250 images from the APTI database) and using a set of 8 images from an Arabic book. The APTI database contains 45.313.600 of both decomposable and non-decomposable word images. In the evaluation, we conduct two tests. The first test is based on usual metrics used in the literature. In the second test, we provide a novel measure for Arabic language, which can be used for other non-Latin languages.
Synthesizing tests for detecting atomicity violations
(2015)

Using thread-safe libraries can help programmers avoid the complexities of multithreading. However, designing libraries that guarantee thread-safety can be challenging. Detecting and eliminating atomicity violations when methods in the libraries are invoked concurrently is vital in building reliable client applications that use the libraries. While there are dynamic analyses to detect atomicity violations, these techniques are critically dependent on effective multithreaded tests. Unfortunately, designing such tests is non-trivial. In this paper, we design a novel and scalable approach for synthesizing multithreaded tests that help detect atomicity violations. The input to the approach is the implementation of the library and a sequential seed testsuite that invokes every method in the library with random parameters. We analyze the execution of the sequential tests, generate variable lock dependencies and construct a set of three accesses which when interleaved suitably in a multithreaded execution can cause an atomicity violation. Subsequently, we identify pairs of method invocations that correspond to these accesses and invoke them concurrently from distinct threads with appropriate objects to help expose atomicity violations. We have incorporated these ideas in our tool, named Intruder, and applied it on multiple open-source Java multithreaded libraries. Intruder is able to synthesize 40 multithreaded tests across nine classes in less than two minutes to detect 79 harmful atomicity violations, including previously unknown violations in thread-safe classes. We also demonstrate the effectiveness of Intruder by comparing the results with other approaches designed for synthesizing multithreaded tests.
Parallel Processing of Dynamic Continuous Queries over Streaming Data Flows
(2015)

More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.